Robust quantum optimizer with full connectivity

TL;DR

This paper proposes a robust quantum optimization architecture using continuous variables and flux quantization, enabling full connectivity and improved resilience against decoherence, demonstrated through simulation of a small NP-hard problem.

Contribution

It introduces a novel quantum optimizer architecture with full connectivity and robustness, overcoming limitations of current superconducting qubit systems.

Findings

Achieves all-to-all connectivity without overhead.

Demonstrates robustness against dissipation through simulation.

Successfully solves a small NP-hard problem instance.

Abstract

Quantum phenomena have the potential to speed up the solution of hard optimization problems. For example quantum annealing, based on the quantum tunneling effect, has recently been shown to scale exponentially better with system size as compared with classical simulated annealing. However, current realizations of quantum annealers with superconducting qubits face two major challenges. First, the connectivity between the qubits is limited, excluding many optimization problems from a direct implementation. Second, decoherence degrades the success probability of the optimization. We address both of these shortcomings and propose an architecture in which the qubits are robustly encoded in continuous variable degrees of freedom. Remarkably, by leveraging the phenomenon of flux quantization, all-to-all connectivity is obtained without overhead. Furthermore, we demonstrate the robustness of this architecture by simulating the optimal solution of a small instance of the NP-hard and fully connected number partitioning problem in the presence of dissipation.